Conversion of Low-resolution Slope Gradient Based on Histogram Matching at Rolling Hilly Area of Northeast China

Zhao Ting; Wu Binglong; Zheng Fenli; Zhao Luyou

doi:10.13961/j.cnki.stbctb.2024.06.016

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Conversion of Low-resolution Slope Gradient Based on Histogram Matching at Rolling Hilly Area of Northeast China

- Conversion of Low-resolution Slope Gradient Based on Histogram Matching at Rolling Hilly Area of Northeast China
- Bulletin of Soiland Water Conservation Vol. 44, Issue 6, Pages: 149-158(2024)
- 作者机构：
  
  1. 西北农林科技大学水土保持研究所, 陕西杨凌,712100
  2. 中国科学院教育部水土保持与生态环境研究中心, 陕西杨凌,712100
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2024.06.016
  CLC： P231;S157.1
- Published：2024
- 稿件说明：
移动端阅览
Zhao Ting, Wu Binglong, Zheng Fenli, et al. Conversion of Low-resolution Slope Gradient Based on Histogram Matching at Rolling Hilly Area of Northeast China[J]. Bulletin of Soiland Water Conservation, 2024, 44(6): 149-158.
DOI：

Zhao Ting, Wu Binglong, Zheng Fenli, et al. Conversion of Low-resolution Slope Gradient Based on Histogram Matching at Rolling Hilly Area of Northeast China[J]. Bulletin of Soiland Water Conservation, 2024, 44(6): 149-158. DOI： 10.13961/j.cnki.stbctb.2024.06.016.

摘要

[目的] 为了克服目前可免费下载的30 m分辨率DEM在提取东北漫川漫岗地貌区耕地坡度时存在的坡度变缓问题，获取精确坡度数据，为东北漫川漫岗地貌类型区土壤侵蚀定量评价提供重要数据支持。[方法] 基于无人机航测影像生成5 cm分辨率DEM并对其重采样获得1，5和12.5 m分辨率的DEM，结合免费下载的30 m分辨率DEM，确定研究区DEM提取坡度的最佳分辨率；基于直方图匹配算法，分坡度段拟合了30 m与最佳分辨率DEM之间的坡度转换模型。[结果] ①5组DEM分辨率提取的坡度信息表明，1和5 m与5 cm分辨率DEM提取的坡度分布频率具有较强的相似性，且5 m分辨率DEM与1∶10 000比例尺地形图的分辨率相对应。据此确定5 m分辨率为构建坡度转换模型的最佳分辨率。②基于直方图匹配算法分坡度段构建了30与5 m分辨率DEM提取坡度的一元一次线性模型和一元二次非线性模型；且当地面坡度小于7°时宜选取线性坡度转换模型，而当地面坡度大于7°时宜选取非线性坡度转换模型。③经线性和非线性坡度转换模型优化后，30 m坡度分布频率与5 m分辨率的坡度分布频率基本相似，且协方差、相关系数均大幅度提高。这说明30 m分辨率DEM提取的坡度信息经模型转换后能够真实反映地面起伏特征，且非线性坡度转换模型优化效果更佳。[结论] 5 m分辨率为研究区DEM提取坡度的最佳分辨率。构建的低—高分辨率的坡度转换模型中，非线性坡度转换模型较线性坡度转换模型的优化效果更好。

Abstract

[Objective] Slope gradient data was accurately obtained to address the limitation of slope gradient underestimation using the freely downloaded 30 m resolution digital elevation model (DEM) for farmland in Northeast China

in order to provide important data support for quantitatively evaluating soil erosion in the rolling hilly regions. [Methods] A 5 cm resolution DEM was generated from drone survey images and resampled to obtain 1

and 12.5 m DEM resolutions. Combined with the 30 m DEM resolution

the optimal DEM resolution for slope gradient extraction in the study area was identified. Additionally

the histogram matching method was used to establish a slope gradient conversion model between the 30 m DEM resolution and the optimal DEM resolution for each slope gradient category. [Results] ① The slope gradient distributions derived from the five DEM resolutions indicated that the 1 m and 5 m DEM resolutions exhibited a strong similarity to the slope gradient distribution of the 5 cm DEM. Given that the 5 m DEM resolution corresponds to a 1∶10

000 scale topographic map

the 5 m DEM resolution was optimal for constructing the slope gradient conversion model. ② Using the histogram matching method

a univariate linear model and a univariate quadratic non-linear model were developed for slope gradient conversion between the 30 m and 5 m DEM resolutions across different slope gradient segments. The linear conversion model was suitable for slopes less than 7°

while the non-linear model was more appropriate for slopes greater than 7°. ③ After applying both linear and non-linear conversion models

the frequency distribution of slope gradients extracted from the 30 m DEM resolution closely matched that of the 5 m DEM resolution

significantly improving covariance and correlation coefficients. This reflected that the slope gradients after conversion from the 30 m DEM resolution can accurately represent ground undulation; additionally

the optimization results from the non-linear conversion model were superior to those from the linear conversion model. [Conclusion] The 5 m DEM resolution is the optimal resolution for extracting slope data in the study area. The developed conversion model for low-to-high resolution slope gradients showed that the non-linear slope conversion model has a better optimization effect than the linear slope conversion model.

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